W ORKING D ATA S ET I NCLUDES 79,000 PATIENT ENCOUNTERS OVER TWO - - PowerPoint PPT Presentation
P REDICTING N O S HOWS IN F AMILY M EDICINE 1 Cole Phillips Jim Grayson, PhD David Newton Anna Ramanathan 1: Potential Publication P RESENTATION O VERVIEW Project Goal Explanation of Data Set and Impact of No Show Rate Visit Specific
Cole Phillips Jim Grayson, PhD David Newton Anna Ramanathan
1: Potential Publication
¢ Project Goal ¢ Explanation of Data Set and Impact of No Show
¢ Visit Specific No Show Predictors Return Visits Hospital Discharge Visits ¢ Which Patients Aren’t Showing Up? ¢ Proposed Interventions to achieve goal ¢ Conclusion and Summary
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16.50% 12% 0% 5% 10% 15% 20% No Show Rate Goal Rate
No Show Excess No Show Rate
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10000 20000 30000 40000 50000 May 2016- Apr 2017 May 2017- May 2018
AU FAMILY MEDICINE CLINIC NO SHOW RATE
Arrival No Show
¢ What’s Included in the Data?
Past No Show Rate Age Appointment day Insurance type Provider type Race Sex Visit type Zip code
Total Scheduled Visits
16.3% No Show Rate 16.7% No Show Rate
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¢ Patients who No Show are at risk of:1,2 Poorly controlled disease states, especially in
Not being up to date on preventative services and
Higher quantity of visits to the emergency
¢ Clinic suffers from patient No Shows3 Lack of continuity of care and disrupted flow Empty slots take up appointment time that could
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$8,000,000 $9,000,000 $10,000,000 $11,000,000 $12,000,000 2016 2017
YEARLY FINANCIAL IMPACT OF HIGH NO SHOW RATE
Total Revenue Now Total Revenue at Goal No Show Rate *Revenue data assumes $80 professional services revenue and $118 facility revenue for every family medicine visit. Then, from historical data, it is assumed that 3% of every patient that comes to clinic will be admitted to the hospital during the year and that every inpatient visit generates $5,444 of additional revenue.
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0% 10% 20% 30% 40% Hospital Discharge Visit New Patient Visit Annual Visit Return Visit Other Same Day Visit
NO SHOW RATE BY VISIT TYPE WITH VOLUMES ABOVE BAR
Current No Show Rate Goal No Show Rate
51,700 scheduled
1,600 scheduled
6,900 scheduled
No Show Rate *Goal No Show Rate
3,100 scheduled
*Other includes Lab Visit, Procedures, Consults, and similar type visits
*
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10000 20000 30000 40000 50000 Return Visit New Patient Visit Hospital Discharge Visit Same Day Visit Annual Visit Other
SHOW AND NO SHOW TOTALS BY VISIT TYPE
No Show Show
1) Return Visits 2) New Patient Visits 3) Hospital Discharge Visits
Initial Focus due to Large Volume
Encounters
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10000 20000 30000 May 2016-Apr 2017 May 2017- May 2018
RETURN VISIT NO SHOW RATES AND VOLUMES BY YEAR
Arrival No Show
16.7% No Show Rate 17.6% No Show Rate
Total Scheduled Visits
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*No Show Rate: Percentage of visits patient didn’t show up to appt. *No Show Delta: Value of missed appts. in relation to made appts.
Variable Importance
10 20 30 40 50 60 70 Y2 Return Delta Y1 No Show Rate Y1 Return No Show Rate Y1 Return Delta Y2 Return No Show Rate
REALTIVE IMPORTANCE OF DIFFERENT PREDICTORS 10
Variable Importance
*No Show Rate: Percentage of visits patient didn’t show up to appt. *No Show Delta: Value of missed appts. in relation to made appts. *Arrivals: How many total appts. a patient showed up for
10 20 30 40 50 60 Y1 HDC Delta Y2 HDC No Show Rate Y2 HDC Delta Day of Week Time of Day Y2 HDC Arrivals Y1 HDC No Show Rate Y1 HDC Arrivals
VARIABLE IMPORTANCE IN PREDICTION 17
85% 15%
PREDICTIVE ACCURACY OF THE MODEL
Correct Prediction Wrong Prediction
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¢ Cohort 1: n= 2,819 (25% of patients), NSR= 28% Patients with 1 No Show in current year ¢ Cohort 2: n= 843 (7.5% of patients), NSR= 37% Patients with 2 No Shows in current year ¢ Cohort 3: n= 527 (5% of patients), NSR= 47% Patients with 3 or more No Shows in current year ¢ Foundations of Interventions: Nudge Theory 4, 5 Practical Staff Reminder Systems 6, 7, 8 Patient Education 9
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Control group – No intervention is employed for these patients, they receive the same reminder letters and reminder messages that every patient receives Crafted Letter – This letter has ‘social norm’ theory language geared at ‘nudging’ patients towards arriving at appointments and was sent at the beginning of the study Crafted Text Message – This text message has abbreviated ‘social norm’ theory language and is sent either 5 days prior or 1 day prior to an appointment depending
Scripted Staff Phone Call – This phone call is performed by the AU staff and is a personal scripted reminder 5 days prior to an appointment
0% 5% 10% 15% 20% 25% 30% PATIENTS WITH 1 NO-SHOW 3 No Show Rate
improvement n=675 n=342 n=733
*Not enough data to be significant yet*
*Control group – no intervention *Received TEXT 5 days prior to appt. *Received LETTER at beginning of study
0% 5% 10% 15% 20% 25% 30% PATIENTS WITH 2 NO-SHOWS 4 No Show Rate
improvement n=302 n=147 n=118
*Not enough data to be significant yet*
*Control group – no intervention *Received LETTER at beginning of study and CALL 5 days prior to appt. *Received LETTER at beginning of study and TEXT 5 days prior to appt.
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% PATIENTS WITH 3 OR MORE NO-SHOWS 5 No Show Rate
improvement n=310 n=88 n=93 n=87
*Significant results achieved with a p-value of < .01 and Power
*Control group – no intervention *Received LETTER at beginning of study and CALL 5 days prior and TEXT 1 day prior to appt. *Received LETTER at beginning of study and TEXT 1 day prior to appt. *Received LETTER at beginning of study and CALL 5 days prior to appt.
Intervention Patient Group Patient # No-Show Rate Control Group 1 No-Show 675
Text Only (5 days) 1 No-Show 342
Letter Only 1 No-Show 733
Control Group 2 No-Shows 302
Letter & Call (5 days) 2 No-Shows 118
Letter & Text (5 days) 2 No-Shows 147
Control Group 3 or more No-Shows 310
Letter & Text (1 day) 3 or more No-Shows 88
Letter & Call (5 days) 3 or more No-Shows 93
Letter & Call (5 days) & Text (1 day) 3 or more No-Shows 87
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